research-article

Foundations of Trust and Distrust in Networks: Extended Structural Balance Theory

Authors:

Sibel AdaliAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 8, Issue 3

Article No.: 13, Pages 1 - 33

https://doi.org/10.1145/2628438

Published: 08 July 2014 Publication History

Abstract

Modeling trust in very large social networks is a hard problem due to the highly noisy nature of these networks that span trust relationships from many different contexts, based on judgments of reliability, dependability, and competence. Furthermore, relationships in these networks vary in their level of strength. In this article, we introduce a novel extension of structural balance theory as a foundational theory of trust and distrust in networks. Our theory preserves the distinctions between trust and distrust as suggested in the literature, but also incorporates the notion of relationship strength that can be expressed as either discrete categorical values, as pairwise comparisons, or as metric distances. Our model is novel, has sound social and psychological basis, and captures the classical balance theory as a special case. We then propose a convergence model, describing how an imbalanced network evolves towards new balance, and formulate the convergence problem of a social network as a Metric Multidimensional Scaling (MDS) optimization problem. Finally, we show how the convergence model can be used to predict edge signs in social networks and justify our theory through extensive experiments on real datasets.

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Zhao QLiu GYang FYang RKou ZWang D(2023)Self-Supervised Signed Graph Attention Network for Social Recommendation2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191310(1-9)Online publication date: 18-Jun-2023
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Index Terms

Foundations of Trust and Distrust in Networks: Extended Structural Balance Theory
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. Information retrieval
    1. Document representation

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 8, Issue 3

June 2014

256 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/2639948

Editor:
Marc Najork
Google

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Copyright © 2014 ACM.

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Publication History

Published: 08 July 2014

Accepted: 01 March 2014

Revised: 01 December 2013

Received: 01 June 2013

Published in TWEB Volume 8, Issue 3

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https://dl.acm.org/doi/10.1609/aaai.v37i4.25565
Zhang ZLiu JZheng XWang YHan PWang YZhao KZhang Z(2023)RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural NetworksProceedings of the ACM Web Conference 202310.1145/3543507.3583221(60-70)Online publication date: 30-Apr-2023
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Zhao QLiu GYang FYang RKou ZWang D(2023)Self-Supervised Signed Graph Attention Network for Social Recommendation2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191310(1-9)Online publication date: 18-Jun-2023
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